Model Overview

We estimate the age of a site by calculating the years since the last fire. We then fit a curve to model the recovery of vegetation (measured using NDVI) as a function of it’s age. An additional level models the parameters of the negative exponential curve as a function of environmental variables. This means that sites with similar environmental conditions should have similar recovery curves.

Workflow

This repository was developed using the Targets framework.

── Attaching packages ─────────────────────────────────────── tidyverse 1.3.1 ── ✔ ggplot2 3.3.5 ✔ purrr 0.3.4 ✔ tibble 3.1.6 ✔ dplyr 1.0.8 ✔ tidyr 1.2.0 ✔ stringr 1.4.0 ✔ readr 2.1.2 ✔ forcats 0.5.1 ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ── ✖ dplyr::filter() masks stats::filter() ✖ dplyr::lag() masks stats::lag()

Attaching package: ‘arrow’

The following object is masked from ‘package:utils’:

timestamp

CmdStan path set to: /home/rstudio/.cmdstanr/cmdstan-2.29.1

Results

Environmental Controls on Ecosystem Recovery

These parameters represent the relationship of the following environmental variables to the recovery trajectory.

## Warning: `gather_()` was deprecated in tidyr 1.2.0.
## Please use `gather()` instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was generated.

Recovery Trajectories

The plot below illustrates some example recovery trajectories. It currently just shows the top 20 cells with the most observations.

Spatial Predictions

Maps of spatial parameters in the model.